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Provides an overview of major developments pertaining to generalized information theory during the lifetime of Kybernetes. Generalized information theory is viewed as a collection of concepts, theorems, principles, and methods for dealing with problems involving uncertainty‐based information that are beyond the narrow scope of classical information theory. Introduces well‐justified measures of uncertainty in fuzzy set theory, possibility theory, and Dempster‐Shafer theory. Shows how these measures are connected with the classical Hartley measure and Shannon entropy. Discusses basic issues regarding some principles of generalized uncertainty‐based information.
It is well known that the only way of making complexity in inductive (data‐driven) systems modelling manageable is to be tolerant of predictive (or retrodictive…
It is well known that the only way of making complexity in inductive (data‐driven) systems modelling manageable is to be tolerant of predictive (or retrodictive) uncertainty in the resulting models. It is argued that two complementary principles — the principles of maximum and minimum uncertainty — are essential to using uncertainty properly to combat complexity. When uncertainty is conceptualised in terms of probability theory, these principles become the well‐established principles of maximum and minimum entropy. When a more general framework of fuzzy measures is employed, uncertainty becomes a multi‐dimensional entity and the maximum and minimum uncertainty principles lead to optimisation problems with multiple objective criteria. Four distinct types of uncertainty are now recognised and their well‐justified measures determined within fuzzy set theory and one subset of fuzzy measures — the Dempster‐Shafer theory of evidence. The uncertainty types and their measures are briefly described.
Presents an overview of currently recognized theories of imprecise probabilities and their possible extensions. It is shown how the theories are ordered by their levels of generality. A summary of current results regarding measures of uncertainty and uncertainty‐based information is also presented.
The concept of information is central to several fields of research and professional practice. So many definitions have been put forward that complete inventory is…
The concept of information is central to several fields of research and professional practice. So many definitions have been put forward that complete inventory is unachievable while authors have failed to reach a consensus. In the face of the present impasse, innovative proposals could rouse information theorists to action, but literature surveys tend to emphasize the common traits of definitions. Reviewers are inclined to iron out originality in information models; thus the purpose of this paper is to discover the creativity of authors attempting to define the concept of information and to stimulate the progress of studies in this field.
Because the present inquiry could be influenced and distorted by personal criteria and opinions, the authors have adopted precise criteria and guidelines. It could be said the present approach approximates a statistical methodology.
The findings of this paper include (1) The authors found 32 original definitions of information which sometimes current surveys have overlooked. (2) The authors found a relation between information theories and advances in information technology. (3) Overall, the authors found that researchers take account of a wide variety of perspectives yet overlook the notion of information as used by computing practitioners such as electronic engineers and software developers.
The authors comment on some limitations of the procedure that was followed. Results 1 and 3 open up new possibilities for theoretical research in the information domain.
This is an attempt to conduct a bibliographical inquiry driven by objective and scientific criteria; its value lies in the fact that final report has not been influenced by personal choice or arbitrary viewpoints.
The XTRA access system is aimed at making the interaction with expert systems much easier for inexperienced users. It communicates with the user in a natural language (German) extracting data relevant to the expert system from the natural language input. It answers users' queries concerning the terminology used and provides what the developers describe as “User‐accommodated natural‐language verbalisations of results and explanations provided by the expert system”. This development is described by J. Allgayer, K. Harbusch, A. Kobsa, C. Reddig, N. Reithinger and D. Schmauks in the International Journal of Man‐Machine Studies, Vol. 31, Part 2, August 1989, pp. 161–95. The developers of this system have introduced a number of novel artificial intelligence techniques. These have included the combination of natural language user input and user gestures on the terminal screen. Four different knowledge sources aid referent identification and simultaneous communication of the access system with the user and the expert system have been incorporated.